When an input (e.g., input signal) passes through a channel, temporal and frequency spreading of the input can occur. An equalizer can be used to process, or equalize, the frequency response of the input to minimize the effects of spreading on the input. The effects of spreading can reduce the integrity of an input enough to prevent a detector from detecting the original input.
Conventional methods of equalization and detection typically use a single equalizer and a single detector. For example, conventional methods of equalization can use a least mean squares (LMS) filter, or a filter that uses a LMS algorithm, to equalize an input. A LMS algorithm can be used to determine the difference between a target input and an actual input. LMS filters attempt to reduce both inter-symbol interference (ISI) and noise. Conventional LMS filters are not, however, optimized to minimize either ISI or noise; therefore, LMS filters cannot guarantee the best bit error rate (BER) performance. As another example, conventional methods of equalization can use a zero forcing (ZF) filter, or a filter that uses a ZF algorithm. A ZF filter uses the reciprocal of a channel response to minimize the ISI created by the channel. Conventionally, the a filter is optimized to minimize ISI (e.g., eliminate ISI), but the ZF filter can substantially amplify noise where the channel response has a small magnitude. In particular, as the channel response (e.g., H(s)) approaches zero, reciprocal of the channel response
  (            e      .      g      .        ,          1              H        ⁡                  (          s          )                      )approaches infinity.